Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States

2016 ◽  
Vol 30 (12) ◽  
pp. 4399-4413 ◽  
Author(s):  
A. Agarwal ◽  
R. Maheswaran ◽  
J Kurths ◽  
R. Khosa
Keyword(s):  
United States ◽  
Wavelet Spectrum ◽  
Western United States ◽  
Self Organizing Maps ◽  
Self Organizing

Fragility curves for toppling of railroad locomotives

2020 ◽  
Vol 36 (4) ◽  
pp. 1623-1644
Author(s):  
Bruce F Maison
Keyword(s):  
United States ◽  
Size Effect ◽  
Case Studies ◽  
Fragility Curves ◽  
Western United States ◽  
Passenger Cars ◽  
Strong Earthquakes ◽  
Ground Shaking ◽  
Seismic Regions

Three locomotives that overturned (toppled) during strong earthquakes (>6.5M) are used as computer analytical case studies. The locomotives were at rest or traveling very slowly at the time of the earthquakes. Fragility curves are presented relating ground shaking intensity to likelihood of toppling. Supplemental studies determine the influence of various parameters, including track gauge, damping, sway-roll period, and size effect. The shaking intensities necessary for standard gauge (56.5 in) locomotives to topple are much greater than the median intensities of 2475-year earthquakes representative of those in high seismic regions of the western United States. A general conclusion is that standard gauge locomotives at rest are not susceptible to toppling in such earthquakes (≪50% chance). This can be expected to be the case as well for freight and passenger cars having sizes and slenderness similar to the case study locomotives. The study also provides insights about the toppling fragility of other large unanchored objects having similar proportions.

Insights into the implementation of synoptic weather-type classification using self-organizing maps: an Australian case study

2014 ◽  
Vol 35 (12) ◽  
pp. 3471-3485 ◽  
Author(s):  
Ningbo Jiang ◽  
Kehui Luo ◽  
Paul J. Beggs ◽  
Kevin Cheung ◽  
Yvonne Scorgie
Keyword(s):  
Weather Type ◽  
Self Organizing Maps ◽  
Synoptic Weather ◽  
Australian Case ◽  
Type Classification ◽  
Self Organizing

Water and Food in the American West

2018 ◽  
pp. 320-343
Author(s):  
Josué Medellín-Azuara ◽  
Jay Lund ◽  
Daniel A. Sumner
Keyword(s):  
United States ◽  
Global Economy ◽  
American West ◽  
Short Review ◽  
The United States ◽  
Western United States ◽  
Water Shortages ◽  
Groundwater Overdraft ◽  
Wide Range

The American West, the last region in the continental United States to be developed for extensive agriculture, is characterized by a wide range of biomes including arid, and semiarid regions, forest, and coastline. In its less water-rich places, this has forced the development of water supply infrastructure for agriculture and cities. The American West rapidly became an agricultural powerhouse to the United States and a major exporter of agricultural commodities in global economy. This chapter reviews agriculture in the western United States, followed by a short review of major western water issues for agriculture, including surface water shortages from drought and persistent groundwater overdraft. The California 2012–2016 drought is used as a case study to identify lessons for future food and fiber production in California, the western United States, and globally.

Predictive Geological Mapping from Geophysical Data Using Self-Organizing Maps: A Case Study from Baie Verte, NL, Canada

Geophysics ◽  
2021 ◽  
pp. 1-55
Author(s):  
Angela Carter-McAuslan ◽  
Colin Farquharson
Keyword(s):  
Gravity Data ◽  
Geological Mapping ◽  
Magnetic Data ◽  
Self Organizing Maps ◽  
Predictive Mapping ◽  
Legacy Data ◽  
Geological Units ◽  
Radiometric Data ◽  
Self Organizing

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multi-variate datasets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We applied SOMs to magnetic, radiometric and gravity datasets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geological maps available for this area and the knowledge from numerous geological studies allowed for assessing the accuracy of the SOM-based predictive mapping. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geological units identified by previous traditional geological mapping. Of the combinations of datasets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the datasets. The SOM results could be used as input into k-means clustering as k-means clustering requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geological mapping would be most beneficial which can be especially useful in parts of the world where access is difficult and expensive.

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